Merge pull request #513 from margarita0303/dev

Added Levenberg-Marquardt algorithm and svd Golub-Kahan

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticDifficultTest.kt

@@ -0,0 +1,90 @@
+/*
+ * Copyright 2018-2023 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
+
+import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.nd.component1
+import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.LMInput
+import space.kscience.kmath.tensors.core.levenbergMarquardt
+import kotlin.math.roundToInt
+
+fun main() {
+    val NData = 200
+    var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
+    for (i in 0 until NData) {
+        t_example[i, 0] = t_example[i, 0] * (i + 1) - 104
+    }
+
+    val Nparams = 15
+    var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_example[i, 0] = p_example[i, 0] + i - 25
+    }
+
+    val exampleNumber = 1
+
+    var y_hat =  funcDifficultForLm(t_example, p_example, exampleNumber)
+
+    var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_init[i, 0] = (p_example[i, 0] + 0.9)
+    }
+
+    var t = t_example
+    val y_dat = y_hat
+    val weight = 1.0 / Nparams * 1.0 - 0.085
+    val dp = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
+    ).as2D()
+    var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / -50.0)
+    val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / 50.0)
+    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-2, 11.0, 9.0, 1.0)
+//    val opts = doubleArrayOf(3.0, 10000.0, 1e-6, 1e-6, 1e-6, 1e-6, 1e-3, 11.0, 9.0, 1.0)
+
+    val inputData = LMInput(::funcDifficultForLm,
+        p_init.as2D(),
+        t,
+        y_dat,
+        weight,
+        dp,
+        p_min.as2D(),
+        p_max.as2D(),
+        opts[1].toInt(),
+        doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
+        doubleArrayOf(opts[6], opts[7], opts[8]),
+        opts[9].toInt(),
+        10,
+        1)
+
+    val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
+
+    println("Parameters:")
+    for (i in 0 until result.resultParameters.shape.component1()) {
+        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
+        print("$x ")
+    }
+    println()
+
+    println("Y true and y received:")
+    var y_hat_after =  funcDifficultForLm(t_example, result.resultParameters, exampleNumber)
+    for (i in 0 until y_hat.shape.component1()) {
+        val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
+        val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
+        println("$x $y")
+    }
+
+    println("Сhi_sq:")
+    println(result.resultChiSq)
+    println("Number of iterations:")
+    println(result.iterations)
+}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticEasyTest.kt

@@ -0,0 +1,57 @@
+/*
+ * Copyright 2018-2023 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
+
+import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.nd.component1
+import space.kscience.kmath.tensors.LevenbergMarquardt.funcDifficultForLm
+import space.kscience.kmath.tensors.LevenbergMarquardt.funcEasyForLm
+import space.kscience.kmath.tensors.LevenbergMarquardt.getStartDataForFuncEasy
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.LMInput
+import space.kscience.kmath.tensors.core.levenbergMarquardt
+import kotlin.math.roundToInt
+
+fun main() {
+    val startedData = getStartDataForFuncEasy()
+    val inputData = LMInput(::funcEasyForLm,
+        DoubleTensorAlgebra.ones(ShapeND(intArrayOf(4, 1))).as2D(),
+        startedData.t,
+        startedData.y_dat,
+        startedData.weight,
+        startedData.dp,
+        startedData.p_min,
+        startedData.p_max,
+        startedData.opts[1].toInt(),
+        doubleArrayOf(startedData.opts[2], startedData.opts[3], startedData.opts[4], startedData.opts[5]),
+        doubleArrayOf(startedData.opts[6], startedData.opts[7], startedData.opts[8]),
+        startedData.opts[9].toInt(),
+        10,
+        startedData.example_number)
+
+    val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
+
+    println("Parameters:")
+    for (i in 0 until result.resultParameters.shape.component1()) {
+        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
+        print("$x ")
+    }
+    println()
+
+    println("Y true and y received:")
+    var y_hat_after =  funcDifficultForLm(startedData.t, result.resultParameters, startedData.example_number)
+    for (i in 0 until startedData.y_dat.shape.component1()) {
+        val x = (startedData.y_dat[i, 0] * 10000).roundToInt() / 10000.0
+        val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
+        println("$x $y")
+    }
+
+    println("Сhi_sq:")
+    println(result.resultChiSq)
+    println("Number of iterations:")
+    println(result.iterations)
+}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StaticLm/staticMiddleTest.kt

@@ -0,0 +1,88 @@
+/*
+ * Copyright 2018-2023 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.LevenbergMarquardt.StaticLm
+
+import space.kscience.kmath.nd.ShapeND
+import space.kscience.kmath.nd.as2D
+import space.kscience.kmath.nd.component1
+import space.kscience.kmath.tensors.LevenbergMarquardt.funcMiddleForLm
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.div
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.LMInput
+import space.kscience.kmath.tensors.core.levenbergMarquardt
+import kotlin.math.roundToInt
+fun main() {
+    val NData = 100
+    var t_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(NData, 1))).as2D()
+    for (i in 0 until NData) {
+        t_example[i, 0] = t_example[i, 0] * (i + 1)
+    }
+
+    val Nparams = 20
+    var p_example = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_example[i, 0] = p_example[i, 0] + i - 25
+    }
+
+    val exampleNumber = 1
+
+    var y_hat =  funcMiddleForLm(t_example, p_example, exampleNumber)
+
+    var p_init = DoubleTensorAlgebra.zeros(ShapeND(intArrayOf(Nparams, 1))).as2D()
+    for (i in 0 until Nparams) {
+        p_init[i, 0] = (p_example[i, 0] + 0.9)
+    }
+
+    var t = t_example
+    val y_dat = y_hat
+    val weight = 1.0
+    val dp = BroadcastDoubleTensorAlgebra.fromArray(
+        ShapeND(intArrayOf(1, 1)), DoubleArray(1) { -0.01 }
+    ).as2D()
+    var p_min = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / -50.0)
+    val p_max = DoubleTensorAlgebra.ones(ShapeND(intArrayOf(Nparams, 1)))
+    p_min = p_min.div(1.0 / 50.0)
+    val opts = doubleArrayOf(3.0, 7000.0, 1e-5, 1e-5, 1e-5, 1e-5, 1e-5, 11.0, 9.0, 1.0)
+
+    val inputData = LMInput(::funcMiddleForLm,
+        p_init.as2D(),
+        t,
+        y_dat,
+        weight,
+        dp,
+        p_min.as2D(),
+        p_max.as2D(),
+        opts[1].toInt(),
+        doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
+        doubleArrayOf(opts[6], opts[7], opts[8]),
+        opts[9].toInt(),
+        10,
+        1)
+
+    val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
+
+    println("Parameters:")
+    for (i in 0 until result.resultParameters.shape.component1()) {
+        val x = (result.resultParameters[i, 0] * 10000).roundToInt() / 10000.0
+        print("$x ")
+    }
+    println()
+
+
+    var y_hat_after =  funcMiddleForLm(t_example, result.resultParameters, exampleNumber)
+    for (i in 0 until y_hat.shape.component1()) {
+        val x = (y_hat[i, 0] * 10000).roundToInt() / 10000.0
+        val y = (y_hat_after[i, 0] * 10000).roundToInt() / 10000.0
+        println("$x $y")
+    }
+
+    println("Сhi_sq:")
+    println(result.resultChiSq)
+    println("Number of iterations:")
+    println(result.iterations)
+}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamLm.kt

@@ -0,0 +1,68 @@
+/*
+ * Copyright 2018-2023 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
+
+import kotlinx.coroutines.delay
+import kotlinx.coroutines.flow.*
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.tensors.LevenbergMarquardt.StartDataLm
+import space.kscience.kmath.tensors.core.BroadcastDoubleTensorAlgebra.zeros
+import space.kscience.kmath.tensors.core.DoubleTensorAlgebra
+import space.kscience.kmath.tensors.core.LMInput
+import space.kscience.kmath.tensors.core.levenbergMarquardt
+import kotlin.random.Random
+import kotlin.reflect.KFunction3
+
+fun streamLm(lm_func: (MutableStructure2D<Double>, MutableStructure2D<Double>, Int) -> (MutableStructure2D<Double>),
+             startData: StartDataLm, launchFrequencyInMs: Long, numberOfLaunches: Int): Flow<MutableStructure2D<Double>> = flow{
+
+    var example_number = startData.example_number
+    var p_init = startData.p_init
+    var t = startData.t
+    var y_dat = startData.y_dat
+    val weight = startData.weight
+    val dp = startData.dp
+    val p_min = startData.p_min
+    val p_max = startData.p_max
+    val opts = startData.opts
+
+    var steps = numberOfLaunches
+    val isEndless = (steps <= 0)
+
+    val inputData = LMInput(lm_func,
+        p_init,
+        t,
+        y_dat,
+        weight,
+        dp,
+        p_min,
+        p_max,
+        opts[1].toInt(),
+        doubleArrayOf(opts[2], opts[3], opts[4], opts[5]),
+        doubleArrayOf(opts[6], opts[7], opts[8]),
+        opts[9].toInt(),
+        10,
+        example_number)
+
+    while (isEndless || steps > 0) {
+        val result = DoubleTensorAlgebra.levenbergMarquardt(inputData)
+        emit(result.resultParameters)
+        delay(launchFrequencyInMs)
+        inputData.realValues = generateNewYDat(y_dat, 0.1)
+        inputData.startParameters = result.resultParameters
+        if (!isEndless) steps -= 1
+    }
+}
+
+fun generateNewYDat(y_dat: MutableStructure2D<Double>, delta: Double): MutableStructure2D<Double>{
+    val n = y_dat.shape.component1()
+    val y_dat_new = zeros(ShapeND(intArrayOf(n, 1))).as2D()
+    for (i in 0 until n) {
+        val randomEps = Random.nextDouble(delta + delta) - delta
+        y_dat_new[i, 0] = y_dat[i, 0] + randomEps
+    }
+    return y_dat_new
+}

examples/src/main/kotlin/space/kscience/kmath/tensors/LevenbergMarquardt/StreamingLm/streamingLmTest.kt

@@ -0,0 +1,32 @@
+/*
+ * Copyright 2018-2023 KMath contributors.
+ * Use of this source code is governed by the Apache 2.0 license that can be found in the license/LICENSE.txt file.
+ */
+
+package space.kscience.kmath.tensors.LevenbergMarquardt.StreamingLm
+
+import space.kscience.kmath.nd.*
+import space.kscience.kmath.tensors.LevenbergMarquardt.*
+import kotlin.math.roundToInt
+
+suspend fun main(){
+    val startData = getStartDataForFuncDifficult()
+    // Создание потока:
+    val lmFlow = streamLm(::funcDifficultForLm, startData, 0,  100)
+    var initialTime = System.currentTimeMillis()
+    var lastTime: Long
+    val launches = mutableListOf<Long>()
+    // Запуск потока
+    lmFlow.collect { parameters ->
+        lastTime = System.currentTimeMillis()
+        launches.add(lastTime - initialTime)
+        initialTime = lastTime
+        for (i in 0 until parameters.shape.component1()) {
+            val x = (parameters[i, 0] * 10000).roundToInt() / 10000.0
+            print("$x ")
+            if (i == parameters.shape.component1() - 1) println()
+        }
+    }
+
+    println("Average without first is: ${launches.subList(1, launches.size - 1).average()}")
+}